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Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The application of wind energy in electric power systems is growing rapidly due to enhanced public concerns to adverse environmental impacts and escalation in energy costs associated with the use of conventional energy sources. Electric power from wind energy is quite different from that of conventional resources. The fundamental difference is that the wind power is intermittent and uncertain. Therefore, it affects the reliability of power system in a different manner from that of the conventional generators. This paper, from available literatures, presents the model of wind farms and the methods of wind speed parameters assessment. Two main categories of methods for evaluating the wind power reliability contribution, i.e., the analytical method and the Monte Carlo simulation method have been reviewed. This paper also summarizes factors affecting the reliability of wind power system, such as wake effect, correlation of output power for different windturbines, effect of windturbine parameters, penetration and environment. An example has been used to illustrate how these factors affect the reliability of wind power system. Finally, mainstream reliability indices for evaluating reliability are introduced. Among these reliability indices, some are recently developed, such as wind generation interrupted energy benefit (WGIEB), wind generation interruption cost benefit (WGICB), Equivalent Capacity Rate (ECR), load carrying capacity benefit ratio (LCCBR). [Copyright &y& Elsevier]